| GOOGL | |
|---|---|
| Date | |
| 2006-01-03 | 217.62 |
| 2006-01-04 | 222.62 |
| 2006-01-05 | 225.62 |
| 2006-01-06 | 232.83 |
| 2006-01-09 | 233.45 |
| ... | ... |
| 2015-12-24 | 765.84 |
| 2015-12-28 | 782.24 |
| 2015-12-29 | 793.96 |
| 2015-12-30 | 790.30 |
| 2015-12-31 | 778.01 |
2517 rows × 1 columns
Matematyczne modelowanie procesów decyzyjnych w finansach
Alternatywne miary ryzyka
- Miara ryzyka powinna się skupiać na ujemnych zwrotach, np. największym spadku (ang. Maximum Drawdown) lub współczynniku Sortino.
Współczynnik Sortino
Wskaźnik Sortino dla akcji Google
rfr = 0.04
target_return = 0
returns = data.pct_change()
negative_returns = returns.loc[returns['GOOGL'] < target_return]
expected_return = returns.mean()
annualized_return = (expected_return+1)**252 - 1
down_std = negative_returns.std()
sortino_ratio = (annualized_return - rfr) / (down_std*np.sqrt(252))Sortino dla akcji Google wynosi ok. 0.702.
Sharpe dla akcji Google wynosi ok. 0.488.
Alternatywne miary ryzyka
Największy spadek (ang. Maximum Drawdown)
Największa procentowa strata od szczytu do dna.
Zależy od wybranego okna czasowego.
Największy spadek (ang. Maximum Drawdown)
# Maksymalne spadki
roll_max = data.rolling(center=False, min_periods=1, window=252).max()
# Dzienny spadek w stosunku do maksimum
daily_draw_down = data/roll_max - 1.0
# Maksymalny dzienny spadek
max_daily_draw_down = daily_draw_down.rolling(center=False, min_periods=1, window=252).min()
# Wykres
plt.figure(figsize=(15,8))
plt.plot(data.index, daily_draw_down, label='Dzienne spadki')
plt.plot(data.index, max_daily_draw_down, label='Maksymalne spadki w okresie 252 dni')
plt.legend()
plt.show()
Pyfolio
Pyfolio
Tear Sheet
| Start date | 2004-01-12 | |
|---|---|---|
| End date | 2009-12-31 | |
| Total months | 71 | |
| Backtest | ||
| Annual return | 8.791% | |
| Cumulative returns | 65.404% | |
| Annual volatility | 26.26% | |
| Sharpe ratio | 0.45 | |
| Calmar ratio | 0.15 | |
| Stability | 0.00 | |
| Max drawdown | -60.391% | |
| Omega ratio | 1.09 | |
| Sortino ratio | 0.66 | |
| Skew | 0.14 | |
| Kurtosis | 5.88 | |
| Tail ratio | 0.99 | |
| Daily value at risk | -3.261% | |
| Worst drawdown periods | Net drawdown in % | Peak date | Valley date | Recovery date | Duration |
|---|---|---|---|---|---|
| 0 | 60.39 | 2007-11-06 | 2009-03-09 | NaT | NaN |
| 1 | 24.10 | 2005-07-28 | 2006-09-07 | 2007-05-22 | 474 |
| 2 | 11.89 | 2004-06-25 | 2004-08-12 | 2004-11-05 | 96 |
| 3 | 10.87 | 2004-11-15 | 2005-04-18 | 2005-07-14 | 174 |
| 4 | 9.51 | 2007-07-16 | 2007-08-06 | 2007-09-13 | 44 |
Pyfolio
Position Tear Sheet
# Mapowanie sektorów
sect_map = {'COST': 'Consumer Goods',
'INTC': 'Technology',
'CERN': 'Healthcare',
'GPS': 'Technology',
'MMM': 'Construction',
'DELL': 'Technology',
'AMD': 'Technology'}
pf.create_position_tear_sheet(returns,positions, sector_mappings=sect_map)| Top 10 long positions of all time | max |
|---|---|
| COST | 90.01% |
| DELL | 85.73% |
| CERN | 83.53% |
| MMM | 82.09% |
| INTC | 78.59% |
| AMD | 75.76% |
| GPS | 62.24% |
| Top 10 short positions of all time | max |
|---|---|
| AMD | -30.12% |
| DELL | -26.58% |
| CERN | -25.51% |
| MMM | -22.62% |
| GPS | -20.09% |
| INTC | -18.47% |
| COST | -16.44% |
| Top 10 positions of all time | max |
|---|---|
| COST | 90.01% |
| DELL | 85.73% |
| CERN | 83.53% |
| MMM | 82.09% |
| INTC | 78.59% |
| AMD | 75.76% |
| GPS | 62.24% |
